diff --git a/tools/microarray/R/calculate-fold-change.R b/tools/microarray/R/calculate-fold-change.R
index b4a744f59..5af588e04 100644
--- a/tools/microarray/R/calculate-fold-change.R
+++ b/tools/microarray/R/calculate-fold-change.R
@@ -1,28 +1,32 @@
-# TOOL calculate-fold-change.R: "Calculate fold change" (Calculates a geometric average of gene expression for replicate chips and then
-# calculates a difference or ratio between the averages. The output fold change will be represented in log2 scale. 
+# TOOL calculate-fold-change.R: "Calculate fold change" (Calculates a geometric or arithmetic average of gene expression for replicate chips and then
+# calculates a difference or ratio between the averages. The output fold change can be represented either in log2 or linear scale. 
 # Note that the tool is applicable only if you have defined two groups of samples, and the reference group is determined by the smaller group number.)
 # INPUT normalized.tsv: normalized.tsv TYPE GENE_EXPRS 
 # INPUT META phenodata.tsv: phenodata.tsv TYPE GENERIC 
 # OUTPUT fold-change.tsv: fold-change.tsv 
 # PARAMETER column: Column TYPE METACOLUMN_SEL DEFAULT group (Phenodata column describing the groups. Samples of which group attribute is NA are excluded from the analysis.)
+# PARAMETER geometric: "Which mean to use" TYPE [geometric: geometric, arithmetic: arithmetic] DEFAULT geometric (Should the geometric or arithmetic mean be used in the calculation of average expression for the sample groups?)
+# PARAMETER outscale: Scale TYPE [log2: log2, linear: linear] DEFAULT log2 (What scale to use for expressing the results. Log2 yields a symmetric distribution around zero with no change being equal to 0, up-regulation taking positive values and down-regulation negative values. Conversely, in linear scale up-regulation is represented by values greater than 1 and down-regulation values being between 0 and 1.)
 
 # JTT 30.7.2007, Calculate fold changes between groups of samples
 # MG, 3.5.2011, added parameters for choosing aritmetic or geometric mean and for choosing linear or log scale
 # OH, 30.11.2011, support for names as group categories
 # MG, 30.11.2011, added parameter do define reference group
 # ML, 18.10.2016
-# ES, 27.06.2021 set mean to geometric and scale to log2
-	# PARAMETER OPTIONAL geometric: "Which mean to use" TYPE [yes:geometric, no:arithmetic] DEFAULT yes (Should the geometric or arithmetic mean be used in the calculation of average expression for the sample groups?)
-	# PARAMETER OPTIONAL scale: Scale TYPE [log2, linear] DEFAULT log2 (What scale to use for expressing the results. Log2 yields a symmetric distribution around zero with no change being equal to 0, up-regulation taking positive values and down-regulation negative values. Conversely, in linear scale 
-	# up-regulation is represented by values greater than 1 and down-regulation values being between 0 and 1.)
+# OH, 09.09.2021, additional parameters for phenodata read.table
+# OH, 09.09.2021, option geometric or arithmetic mean and option output log2 or linear
+
+if( ! exists("geometric", mode="character") ) {
+	geometric <- "geometric"
+}
+if( ! exists("outscale", mode="character") ) {
+	outscale <- "log2"
+}
 
-	
-geometric<- "yes"
-scale<- "log2"
 # Loads the normalized data and phenodata
 file<-c("normalized.tsv")
 dat<-read.table(file, header=T, sep="\t", row.names=1)
-phenodata<-read.table("phenodata.tsv", header=T, sep="\t")
+phenodata<-read.table("phenodata.tsv", header=T, sep="\t", quote='', as.is=TRUE, check.names=FALSE, comment.char='')
 
 #remove rows that are NAs
 na_samples <- phenodata[which(is.na(phenodata[,pmatch(column,colnames(phenodata))])), 1]
@@ -46,10 +50,10 @@ if(length(unique(groups))>2) {
 	stop("CHIPSTER-NOTE: You have more than two groups! I don't know how to calculate fold change.")
 }
 
-# If arithmetic mean, then transform values to linear scale, arithmetic option removed
-#if (geometric == "no") {
-	#"dat2 <- as.data.frame (2^dat2)
-#}
+# If arithmetic mean, then transform values to linear scale
+if (geometric == "arithmetic") {
+	dat2 <- as.data.frame (2^dat2)
+}
 
 # Calculating averages
 columnnames<-c()
@@ -70,21 +74,21 @@ rownames(dat3)<-rownames(dat2)
 
 # Calculating the fold change
 # Treatment divided by the control
-if (geometric == "yes") {
+if (geometric == "geometric") {
 	FC <- dat3[,2]-dat3[,1]
-} #else {
-	#FC <- dat3[,2] / dat3 [,}
-
+} else {
+	FC <- dat3[,2] / dat3[,1]
+}
 
-# If arithmetic mean and log2 scale, then transform values back to log2 scale, arithmetic mean removed 
-#if (geometric == "no" && scale == "log2") {
-#	FC <- log2(FC)
-#}
+# If arithmetic mean and log2 scale, then transform values back to log2 scale
+if (geometric == "arithmetic" && outscale == "log2") {
+	FC <- log2(FC)
+}
 
-# If geometric mean and linear scale, then transform values to linear scale,  linear scale removed
-#if (geometric == "yes" && scale == "linear") {
-#	FC <- 2^FC
-#}
+# If geometric mean and linear scale, then transform values to linear scale
+if (geometric == "geometric" && outscale == "linear") {
+	FC <- 2^FC
+}
 
 # Saving the results
 write.table(data.frame(dat, FC=FC), file="fold-change.tsv", sep="\t", row.names=T, col.names=T, quote=F)